Benchmarking taxonomic assignment pipelines

Scripts for benchmarking taxonomic analysis tools and databases used in the following publication:

A Almeida, AL Mitchell, A Tarkowska, RD Finn (2018). Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments. GigaScience, Volume 7, Issue 5, 1 May 2018.

Requirements:

• Python 2.7
• BioPython
• R 3.4.1
• R libraries: ggplot2, phyloseq, vegan, scales, grid, ape, RColorBrewer, data.table

Python_scripts / random_species.py

Python script for randomly selecting a set number of species from a taxonomy file.

Usage:

python random_species.py -i [tax.file] -s [number of species] -m [max species per genera]

Options:
-i TAX Input taxonomy file (see example below)
-s SPECIES Number of species to select
-m MAXSPECIES Maximum number of species allowed per genera

The taxonomy file should consist of two columns: the first with the sequence identifier and the second with the full taxonomy lineage.

e.g.

AWVO01000035.3882.5400	sk__Bacteria;k__;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Oscillospiraceae;g__Oscillibacter;s__Oscillibacter_sp._KLE_1745
ADLK01000048.472.1991	sk__Bacteria;k__;p__Firmicutes;c__Clostridia;o__Clostridiales;f__Lachnospiraceae;g__Lachnoclostridium;s__[Clostridium]_citroniae
CVPA01000001.8833.10362	sk__Bacteria;k__;p__Firmicutes;c__Bacilli;o__Bacillales;f__Bacillaceae;g__Bacillus;s__Bacillus_sp._Co1-6
KM360064.1.1408	sk__Bacteria;k__;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Porphyromonas;s__Porphyromonas_katsikii
JUZZ01000099.289.1808	sk__Bacteria;k__;p__Proteobacteria;c__Gammaproteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Haemophilus;s__Haemophilus_parainfluenzae
ADCW01000016.45.1587	sk__Bacteria;k__;p__Firmicutes;c__Negativicutes;o__Veillonellales;f__Veillonellaceae;g__Veillonella;s__Veillonella_sp._6_1_27

Python_scripts / mutate_seqs.py

Python script for generating simulated sequence data with a certain percentage of nucleotide substitutions.

Usage:

python mutate_seqs.py -i [in.fasta] -p [perc]

Options:
-i FASTA Input FASTA file
-p PERC Base substitutions (in %)

R_scripts /

• plot_ds.R : calculate dissimilarity scores and plot heatmap
• plot_f-score.R : calculate recall, precision and F-scores and plot scatterplot
• plot_pcoa.R : plot principal coordinates analysis (PCoA)
• plot_stack.R : plot stacked histogram with predicted genera
• plot_usage.R : plot bargraph with computational cost